{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will learn how to construct a simple machine learning model given a labelled dataset. We will be analysing the Indian Liver Patient Records Dataset, and we will be predicting whether a patient has a liver disease or not. Below you will see that some cells in the notebook contain the commented line *# TODO* for you to add your own code. Try to get this code working yourself and see how you get on. There are many resources on the Web that will help you. Sample code will be provided in an \"Answers\" version of this notebook at a later date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, we will be analyzing the data given to us. It gives us an idea of what features are important to the determination of liver disease. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>208</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>210</td>\n",
       "      <td>51</td>\n",
       "      <td>59</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "      <td>56</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>310</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>74</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>214</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>8.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>342</td>\n",
       "      <td>168</td>\n",
       "      <td>441</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>293</td>\n",
       "      <td>232</td>\n",
       "      <td>245</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>610</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>482</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>542</td>\n",
       "      <td>116</td>\n",
       "      <td>66</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>289</td>\n",
       "      <td>875</td>\n",
       "      <td>731</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>240</td>\n",
       "      <td>1680</td>\n",
       "      <td>850</td>\n",
       "      <td>7.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>232</td>\n",
       "      <td>58</td>\n",
       "      <td>140</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>73</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>220</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>290</td>\n",
       "      <td>139</td>\n",
       "      <td>87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>180</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>189</td>\n",
       "      <td>80</td>\n",
       "      <td>125</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>275</td>\n",
       "      <td>382</td>\n",
       "      <td>330</td>\n",
       "      <td>7.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>390</td>\n",
       "      <td>75</td>\n",
       "      <td>138</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>356</td>\n",
       "      <td>321</td>\n",
       "      <td>562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>315</td>\n",
       "      <td>233</td>\n",
       "      <td>384</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>17.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>388</td>\n",
       "      <td>173</td>\n",
       "      <td>367</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>298</td>\n",
       "      <td>31</td>\n",
       "      <td>83</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>143</td>\n",
       "      <td>22</td>\n",
       "      <td>143</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>191</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>251</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>200</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>268</td>\n",
       "      <td>213</td>\n",
       "      <td>168</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>236</td>\n",
       "      <td>131</td>\n",
       "      <td>90</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>90</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>215</td>\n",
       "      <td>46</td>\n",
       "      <td>134</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>134</td>\n",
       "      <td>54</td>\n",
       "      <td>125</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>612</td>\n",
       "      <td>50</td>\n",
       "      <td>88</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>515</td>\n",
       "      <td>48</td>\n",
       "      <td>92</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>560</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>289</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>190</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65  Female              0.7               0.1                   187   \n",
       "1     62    Male             10.9               5.5                   699   \n",
       "2     62    Male              7.3               4.1                   490   \n",
       "3     58    Male              1.0               0.4                   182   \n",
       "4     72    Male              3.9               2.0                   195   \n",
       "5     46    Male              1.8               0.7                   208   \n",
       "6     26  Female              0.9               0.2                   154   \n",
       "7     29  Female              0.9               0.3                   202   \n",
       "8     17    Male              0.9               0.3                   202   \n",
       "9     55    Male              0.7               0.2                   290   \n",
       "10    57    Male              0.6               0.1                   210   \n",
       "11    72    Male              2.7               1.3                   260   \n",
       "12    64    Male              0.9               0.3                   310   \n",
       "13    74  Female              1.1               0.4                   214   \n",
       "14    61    Male              0.7               0.2                   145   \n",
       "15    25    Male              0.6               0.1                   183   \n",
       "16    38    Male              1.8               0.8                   342   \n",
       "17    33    Male              1.6               0.5                   165   \n",
       "18    40  Female              0.9               0.3                   293   \n",
       "19    40  Female              0.9               0.3                   293   \n",
       "20    51    Male              2.2               1.0                   610   \n",
       "21    51    Male              2.9               1.3                   482   \n",
       "22    62    Male              6.8               3.0                   542   \n",
       "23    40    Male              1.9               1.0                   231   \n",
       "24    63    Male              0.9               0.2                   194   \n",
       "25    34    Male              4.1               2.0                   289   \n",
       "26    34    Male              4.1               2.0                   289   \n",
       "27    34    Male              6.2               3.0                   240   \n",
       "28    20    Male              1.1               0.5                   128   \n",
       "29    84  Female              0.7               0.2                   188   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "553   46    Male             10.2               4.2                   232   \n",
       "554   73    Male              1.8               0.9                   220   \n",
       "555   55    Male              0.8               0.2                   290   \n",
       "556   51    Male              0.7               0.1                   180   \n",
       "557   51    Male              2.9               1.2                   189   \n",
       "558   51    Male              4.0               2.5                   275   \n",
       "559   26    Male             42.8              19.7                   390   \n",
       "560   66    Male             15.2               7.7                   356   \n",
       "561   66    Male             16.6               7.6                   315   \n",
       "562   66    Male             17.3               8.5                   388   \n",
       "563   64    Male              1.4               0.5                   298   \n",
       "564   38  Female              0.6               0.1                   165   \n",
       "565   43    Male             22.5              11.8                   143   \n",
       "566   50  Female              1.0               0.3                   191   \n",
       "567   52    Male              2.7               1.4                   251   \n",
       "568   20  Female             16.7               8.4                   200   \n",
       "569   16    Male              7.7               4.1                   268   \n",
       "570   16    Male              2.6               1.2                   236   \n",
       "571   90    Male              1.1               0.3                   215   \n",
       "572   32    Male             15.6               9.5                   134   \n",
       "573   32    Male              3.7               1.6                   612   \n",
       "574   32    Male             12.1               6.0                   515   \n",
       "575   32    Male             25.0              13.7                   560   \n",
       "576   32    Male             15.0               8.2                   289   \n",
       "577   32    Male             12.7               8.4                   190   \n",
       "578   60    Male              0.5               0.1                   500   \n",
       "579   40    Male              0.6               0.1                    98   \n",
       "580   52    Male              0.8               0.2                   245   \n",
       "581   31    Male              1.3               0.5                   184   \n",
       "582   38    Male              1.0               0.3                   216   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "5                          19                          14             7.6   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "9                          53                          58             6.8   \n",
       "10                         51                          59             5.9   \n",
       "11                         31                          56             7.4   \n",
       "12                         61                          58             7.0   \n",
       "13                         22                          30             8.1   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "16                        168                         441             7.6   \n",
       "17                         15                          23             7.3   \n",
       "18                        232                         245             6.8   \n",
       "19                        232                         245             6.8   \n",
       "20                         17                          28             7.3   \n",
       "21                         22                          34             7.0   \n",
       "22                        116                          66             6.4   \n",
       "23                         16                          55             4.3   \n",
       "24                         52                          45             6.0   \n",
       "25                        875                         731             5.0   \n",
       "26                        875                         731             5.0   \n",
       "27                       1680                         850             7.2   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "..                        ...                         ...             ...   \n",
       "553                        58                         140             7.0   \n",
       "554                        20                          43             6.5   \n",
       "555                       139                          87             7.0   \n",
       "556                        25                          27             6.1   \n",
       "557                        80                         125             6.2   \n",
       "558                       382                         330             7.5   \n",
       "559                        75                         138             7.5   \n",
       "560                       321                         562             6.5   \n",
       "561                       233                         384             6.9   \n",
       "562                       173                         367             7.8   \n",
       "563                        31                          83             7.2   \n",
       "564                        22                          34             5.9   \n",
       "565                        22                         143             6.6   \n",
       "566                        22                          31             7.8   \n",
       "567                        20                          40             6.0   \n",
       "568                        91                         101             6.9   \n",
       "569                       213                         168             7.1   \n",
       "570                       131                          90             5.4   \n",
       "571                        46                         134             6.9   \n",
       "572                        54                         125             5.6   \n",
       "573                        50                          88             6.2   \n",
       "574                        48                          92             6.6   \n",
       "575                        41                          88             7.9   \n",
       "576                        58                          80             5.3   \n",
       "577                        28                          47             5.4   \n",
       "578                        20                          34             5.9   \n",
       "579                        35                          31             6.0   \n",
       "580                        48                          49             6.4   \n",
       "581                        29                          32             6.8   \n",
       "582                        21                          24             7.3   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.90        1  \n",
       "1        3.2                        0.74        1  \n",
       "2        3.3                        0.89        1  \n",
       "3        3.4                        1.00        1  \n",
       "4        2.4                        0.40        1  \n",
       "5        4.4                        1.30        1  \n",
       "6        3.5                        1.00        1  \n",
       "7        3.6                        1.10        1  \n",
       "8        4.1                        1.20        2  \n",
       "9        3.4                        1.00        1  \n",
       "10       2.7                        0.80        1  \n",
       "11       3.0                        0.60        1  \n",
       "12       3.4                        0.90        2  \n",
       "13       4.1                        1.00        1  \n",
       "14       2.7                        0.87        1  \n",
       "15       2.3                        0.70        2  \n",
       "16       4.4                        1.30        1  \n",
       "17       3.5                        0.92        2  \n",
       "18       3.1                        0.80        1  \n",
       "19       3.1                        0.80        1  \n",
       "20       2.6                        0.55        1  \n",
       "21       2.4                        0.50        1  \n",
       "22       3.1                        0.90        1  \n",
       "23       1.6                        0.60        1  \n",
       "24       3.9                        1.85        2  \n",
       "25       2.7                        1.10        1  \n",
       "26       2.7                        1.10        1  \n",
       "27       4.0                        1.20        1  \n",
       "28       1.9                        0.95        2  \n",
       "29       3.2                        1.10        2  \n",
       "..       ...                         ...      ...  \n",
       "553      2.7                        0.60        1  \n",
       "554      3.0                        0.80        1  \n",
       "555      3.0                        0.70        1  \n",
       "556      3.1                        1.00        1  \n",
       "557      3.1                        1.00        1  \n",
       "558      4.0                        1.10        1  \n",
       "559      2.6                        0.50        1  \n",
       "560      2.2                        0.40        1  \n",
       "561      2.0                        0.40        1  \n",
       "562      2.6                        0.50        1  \n",
       "563      2.6                        0.50        1  \n",
       "564      2.9                        0.90        2  \n",
       "565      2.1                        0.46        1  \n",
       "566      4.0                        1.00        2  \n",
       "567      1.7                        0.39        1  \n",
       "568      3.5                        1.02        1  \n",
       "569      4.0                        1.20        1  \n",
       "570      2.6                        0.90        1  \n",
       "571      3.0                        0.70        1  \n",
       "572      4.0                        2.50        1  \n",
       "573      1.9                        0.40        1  \n",
       "574      2.4                        0.50        1  \n",
       "575      2.5                        2.50        1  \n",
       "576      2.2                        0.70        1  \n",
       "577      2.6                        0.90        1  \n",
       "578      1.6                        0.37        2  \n",
       "579      3.2                        1.10        1  \n",
       "580      3.2                        1.00        1  \n",
       "581      3.4                        1.00        1  \n",
       "582      4.4                        1.50        2  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"data.csv\")   # Reading data csv file\n",
    "labels = data['Dataset']         # Setting the labels\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see below, there are 9 columns, each with largely different ranges. We can observe that there are a total of 583 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>583.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.746141</td>\n",
       "      <td>3.298799</td>\n",
       "      <td>1.486106</td>\n",
       "      <td>290.576329</td>\n",
       "      <td>80.713551</td>\n",
       "      <td>109.910806</td>\n",
       "      <td>6.483190</td>\n",
       "      <td>3.141852</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1.286449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.189833</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>2.808498</td>\n",
       "      <td>242.937989</td>\n",
       "      <td>182.620356</td>\n",
       "      <td>288.918529</td>\n",
       "      <td>1.085451</td>\n",
       "      <td>0.795519</td>\n",
       "      <td>0.319592</td>\n",
       "      <td>0.452490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "count  583.000000       583.000000        583.000000            583.000000   \n",
       "mean    44.746141         3.298799          1.486106            290.576329   \n",
       "std     16.189833         6.209522          2.808498            242.937989   \n",
       "min      4.000000         0.400000          0.100000             63.000000   \n",
       "25%     33.000000         0.800000          0.200000            175.500000   \n",
       "50%     45.000000         1.000000          0.300000            208.000000   \n",
       "75%     58.000000         2.600000          1.300000            298.000000   \n",
       "max     90.000000        75.000000         19.700000           2110.000000   \n",
       "\n",
       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "count                583.000000                  583.000000      583.000000   \n",
       "mean                  80.713551                  109.910806        6.483190   \n",
       "std                  182.620356                  288.918529        1.085451   \n",
       "min                   10.000000                   10.000000        2.700000   \n",
       "25%                   23.000000                   25.000000        5.800000   \n",
       "50%                   35.000000                   42.000000        6.600000   \n",
       "75%                   60.500000                   87.000000        7.200000   \n",
       "max                 2000.000000                 4929.000000        9.600000   \n",
       "\n",
       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  \n",
       "count  583.000000                  579.000000  583.000000  \n",
       "mean     3.141852                    0.947064    1.286449  \n",
       "std      0.795519                    0.319592    0.452490  \n",
       "min      0.900000                    0.300000    1.000000  \n",
       "25%      2.600000                    0.700000    1.000000  \n",
       "50%      3.100000                    0.930000    1.000000  \n",
       "75%      3.800000                    1.100000    2.000000  \n",
       "max      5.500000                    2.800000    2.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      "Age                           583 non-null int64\n",
      "Gender                        583 non-null object\n",
      "Total_Bilirubin               583 non-null float64\n",
      "Direct_Bilirubin              583 non-null float64\n",
      "Alkaline_Phosphotase          583 non-null int64\n",
      "Alamine_Aminotransferase      583 non-null int64\n",
      "Aspartate_Aminotransferase    583 non-null int64\n",
      "Total_Protiens                583 non-null float64\n",
      "Albumin                       583 non-null float64\n",
      "Albumin_and_Globulin_Ratio    579 non-null float64\n",
      "Dataset                       583 non-null int64\n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Like Describe above other commands for exploring dataset are:\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(n=3) # By default n is 5. It gives the first n rows.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Querying the dataset\n",
    "\n",
    "In order to get a certain idea of how the dataset is distributed, we can try querying the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Pandas you can do almost anything that you can do in SQL (if you are familiar with SQL, e.g., you have seen this kind of thing in a database course). Commands like groupby, join, concatenation, merging, etc. It is also possible to write subqueries and joins in Pandas. See the following queries for a sort of SQL query. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients who are male and are less than 20 years old: 29\n"
     ]
    }
   ],
   "source": [
    "no_patients = len(data[(data['Gender']=='Male') & (data['Age']<20)])\n",
    "print(\"Number of patients who are male and are less than 20 years old: {}\"\n",
    "      .format(no_patients))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some queries for you to practice. Also note the way the print statement with format has been written, which may  be useful in future. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1. Print the number of male patients and number of female patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of male patients: 441\n",
      "Number of male patients: 142\n"
     ]
    }
   ],
   "source": [
    "#TODO\n",
    "no_males = len(data[(data['Gender']=='Male')])\n",
    "no_females = len(data[(data['Gender']=='Female')])\n",
    "\n",
    "print(\"Number of male patients: {}\".format(no_males))\n",
    "print(\"Number of male patients: {}\".format(no_females))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. Print the number of patients who are older than 50 and have a level of Direct_Bilirubin above 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients who are older than 50 and have a level of Direct_Bilirubin above 0.5: 90\n"
     ]
    }
   ],
   "source": [
    "#TODO\n",
    "no_patients = len(data[(data[\"Age\"]>50) & (data[\"Direct_Bilirubin\"] > 0.5)])\n",
    "print(\"Number of patients who are older than 50 and have a level of Direct_Bilirubin above 0.5: {}\"\n",
    "      .format(no_patients))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3. Print a dataframe of patients who are younger than 32 or have a level of Alkaline_Phosphotase below 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>154</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>202</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>183</td>\n",
       "      <td>91</td>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>165</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>194</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>84</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>188</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>190</td>\n",
       "      <td>45</td>\n",
       "      <td>111</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>156</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>187</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>482</td>\n",
       "      <td>102</td>\n",
       "      <td>80</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>145</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>168</td>\n",
       "      <td>33</td>\n",
       "      <td>50</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>160</td>\n",
       "      <td>42</td>\n",
       "      <td>110</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>32</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>176</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>170</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>161</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>198</td>\n",
       "      <td>86</td>\n",
       "      <td>150</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>48</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>175</td>\n",
       "      <td>24</td>\n",
       "      <td>54</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>31</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>158</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>158</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>158</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>215</td>\n",
       "      <td>50</td>\n",
       "      <td>28</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>26.3</td>\n",
       "      <td>12.1</td>\n",
       "      <td>108</td>\n",
       "      <td>168</td>\n",
       "      <td>630</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>185</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>137</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>156</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>138</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>162</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>395</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>97</td>\n",
       "      <td>85</td>\n",
       "      <td>78</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>114</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>198</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>173</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>45</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>153</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>20.2</td>\n",
       "      <td>11.7</td>\n",
       "      <td>188</td>\n",
       "      <td>47</td>\n",
       "      <td>32</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>172</td>\n",
       "      <td>25</td>\n",
       "      <td>41</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>160</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>196</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>180</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>51</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>189</td>\n",
       "      <td>80</td>\n",
       "      <td>125</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>42.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>390</td>\n",
       "      <td>75</td>\n",
       "      <td>138</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>38</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>165</td>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>143</td>\n",
       "      <td>22</td>\n",
       "      <td>143</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>50</td>\n",
       "      <td>Female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>191</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>200</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>268</td>\n",
       "      <td>213</td>\n",
       "      <td>168</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>236</td>\n",
       "      <td>131</td>\n",
       "      <td>90</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>134</td>\n",
       "      <td>54</td>\n",
       "      <td>125</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>190</td>\n",
       "      <td>28</td>\n",
       "      <td>47</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65  Female              0.7               0.1                   187   \n",
       "3     58    Male              1.0               0.4                   182   \n",
       "4     72    Male              3.9               2.0                   195   \n",
       "6     26  Female              0.9               0.2                   154   \n",
       "7     29  Female              0.9               0.3                   202   \n",
       "8     17    Male              0.9               0.3                   202   \n",
       "14    61    Male              0.7               0.2                   145   \n",
       "15    25    Male              0.6               0.1                   183   \n",
       "17    33    Male              1.6               0.5                   165   \n",
       "24    63    Male              0.9               0.2                   194   \n",
       "28    20    Male              1.1               0.5                   128   \n",
       "29    84  Female              0.7               0.2                   188   \n",
       "30    57    Male              4.0               1.9                   190   \n",
       "31    52    Male              0.9               0.2                   156   \n",
       "32    57    Male              1.0               0.3                   187   \n",
       "35    30    Male              1.3               0.4                   482   \n",
       "36    17  Female              0.7               0.2                   145   \n",
       "40    45    Male              2.4               1.1                   168   \n",
       "41    62    Male              0.6               0.1                   160   \n",
       "46    21    Male              3.9               1.8                   150   \n",
       "48    32  Female              0.6               0.1                   176   \n",
       "50    45  Female              0.7               0.2                   170   \n",
       "51    34  Female              0.6               0.1                   161   \n",
       "53    38    Male              1.1               0.3                   198   \n",
       "56    33    Male              0.8               0.2                   198   \n",
       "57    48  Female              0.9               0.2                   175   \n",
       "59    64    Male              1.1               0.5                   145   \n",
       "60    31  Female              0.8               0.2                   158   \n",
       "61    58    Male              1.0               0.5                   158   \n",
       "62    58    Male              1.0               0.5                   158   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "517   28    Male              0.9               0.2                   215   \n",
       "519   35    Male             26.3              12.1                   108   \n",
       "522   46  Female              0.8               0.2                   185   \n",
       "523   50    Male              0.6               0.2                   137   \n",
       "524   29    Male              0.8               0.2                   156   \n",
       "530   22  Female              1.1               0.3                   138   \n",
       "532   62    Male              0.7               0.2                   162   \n",
       "537   10  Female              0.8               0.1                   395   \n",
       "538   52    Male              1.8               0.8                    97   \n",
       "540   42    Male              0.8               0.2                   114   \n",
       "541   42    Male              0.8               0.2                   198   \n",
       "542   62    Male              0.7               0.2                   173   \n",
       "545   45  Female              0.7               0.2                   153   \n",
       "546   45    Male             20.2              11.7                   188   \n",
       "550   46    Male              3.3               1.5                   172   \n",
       "551   29    Male              1.2               0.4                   160   \n",
       "552   45    Male              0.6               0.1                   196   \n",
       "556   51    Male              0.7               0.1                   180   \n",
       "557   51    Male              2.9               1.2                   189   \n",
       "559   26    Male             42.8              19.7                   390   \n",
       "564   38  Female              0.6               0.1                   165   \n",
       "565   43    Male             22.5              11.8                   143   \n",
       "566   50  Female              1.0               0.3                   191   \n",
       "568   20  Female             16.7               8.4                   200   \n",
       "569   16    Male              7.7               4.1                   268   \n",
       "570   16    Male              2.6               1.2                   236   \n",
       "572   32    Male             15.6               9.5                   134   \n",
       "577   32    Male             12.7               8.4                   190   \n",
       "579   40    Male              0.6               0.1                    98   \n",
       "581   31    Male              1.3               0.5                   184   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "6                          16                          12             7.0   \n",
       "7                          14                          11             6.7   \n",
       "8                          22                          19             7.4   \n",
       "14                         53                          41             5.8   \n",
       "15                         91                          53             5.5   \n",
       "17                         15                          23             7.3   \n",
       "24                         52                          45             6.0   \n",
       "28                         20                          30             3.9   \n",
       "29                         13                          21             6.0   \n",
       "30                         45                         111             5.2   \n",
       "31                         35                          44             4.9   \n",
       "32                         19                          23             5.2   \n",
       "35                        102                          80             6.9   \n",
       "36                         18                          36             7.2   \n",
       "40                         33                          50             5.1   \n",
       "41                         42                         110             4.9   \n",
       "46                         36                          27             6.8   \n",
       "48                         39                          28             6.0   \n",
       "50                         21                          14             5.7   \n",
       "51                         15                          19             6.6   \n",
       "53                         86                         150             6.3   \n",
       "56                         26                          23             8.0   \n",
       "57                         24                          54             5.5   \n",
       "59                         20                          24             5.5   \n",
       "60                         21                          16             6.0   \n",
       "61                         37                          43             7.2   \n",
       "62                         37                          43             7.2   \n",
       "..                        ...                         ...             ...   \n",
       "517                        50                          28             8.0   \n",
       "519                       168                         630             9.2   \n",
       "522                        24                          15             7.9   \n",
       "523                        15                          16             4.8   \n",
       "524                        12                          15             6.8   \n",
       "530                        14                          21             7.0   \n",
       "532                        12                          17             8.2   \n",
       "537                        25                          75             7.6   \n",
       "538                        85                          78             6.4   \n",
       "540                        21                          23             7.0   \n",
       "541                        29                          19             6.6   \n",
       "542                        46                          47             7.3   \n",
       "545                        41                          42             4.5   \n",
       "546                        47                          32             5.4   \n",
       "550                        25                          41             5.6   \n",
       "551                        20                          22             6.2   \n",
       "552                        29                          30             5.8   \n",
       "556                        25                          27             6.1   \n",
       "557                        80                         125             6.2   \n",
       "559                        75                         138             7.5   \n",
       "564                        22                          34             5.9   \n",
       "565                        22                         143             6.6   \n",
       "566                        22                          31             7.8   \n",
       "568                        91                         101             6.9   \n",
       "569                       213                         168             7.1   \n",
       "570                       131                          90             5.4   \n",
       "572                        54                         125             5.6   \n",
       "577                        28                          47             5.4   \n",
       "579                        35                          31             6.0   \n",
       "581                        29                          32             6.8   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.90        1  \n",
       "3        3.4                        1.00        1  \n",
       "4        2.4                        0.40        1  \n",
       "6        3.5                        1.00        1  \n",
       "7        3.6                        1.10        1  \n",
       "8        4.1                        1.20        2  \n",
       "14       2.7                        0.87        1  \n",
       "15       2.3                        0.70        2  \n",
       "17       3.5                        0.92        2  \n",
       "24       3.9                        1.85        2  \n",
       "28       1.9                        0.95        2  \n",
       "29       3.2                        1.10        2  \n",
       "30       1.5                        0.40        1  \n",
       "31       2.9                        1.40        1  \n",
       "32       2.9                        1.20        2  \n",
       "35       3.3                        0.90        1  \n",
       "36       3.9                        1.18        2  \n",
       "40       2.6                        1.00        1  \n",
       "41       2.6                        1.10        2  \n",
       "46       3.9                        1.34        1  \n",
       "48       3.0                        1.00        1  \n",
       "50       2.5                        0.70        1  \n",
       "51       3.4                        1.00        1  \n",
       "53       3.5                        1.20        1  \n",
       "56       4.0                        1.00        2  \n",
       "57       2.7                        0.90        2  \n",
       "59       3.2                        1.39        2  \n",
       "60       3.0                        1.00        1  \n",
       "61       3.6                        1.00        1  \n",
       "62       3.6                        1.00        1  \n",
       "..       ...                         ...      ...  \n",
       "517      4.0                        1.00        1  \n",
       "519      2.0                        0.30        1  \n",
       "522      3.7                        0.80        1  \n",
       "523      2.6                        1.10        1  \n",
       "524      3.7                        1.10        2  \n",
       "530      3.8                        1.10        2  \n",
       "532      3.2                        0.60        2  \n",
       "537      3.6                        0.90        1  \n",
       "538      2.7                        0.70        1  \n",
       "540      3.0                        0.70        2  \n",
       "541      3.0                        0.80        2  \n",
       "542      4.1                        1.20        2  \n",
       "545      2.2                        0.90        2  \n",
       "546      2.3                        0.70        1  \n",
       "550      2.4                        0.70        1  \n",
       "551      3.0                        0.90        2  \n",
       "552      2.9                        1.00        1  \n",
       "556      3.1                        1.00        1  \n",
       "557      3.1                        1.00        1  \n",
       "559      2.6                        0.50        1  \n",
       "564      2.9                        0.90        2  \n",
       "565      2.1                        0.46        1  \n",
       "566      4.0                        1.00        2  \n",
       "568      3.5                        1.02        1  \n",
       "569      4.0                        1.20        1  \n",
       "570      2.6                        0.90        1  \n",
       "572      4.0                        2.50        1  \n",
       "577      2.6                        0.90        1  \n",
       "579      3.2                        1.10        1  \n",
       "581      3.4                        1.00        1  \n",
       "\n",
       "[321 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TODO\n",
    "patients = data[(data[\"Age\"] < 32) | (data[\"Alkaline_Phosphotase\"] < 200)]\n",
    "\n",
    "patients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to try out some other queries here. The way above queries have been written try something out on the same lines :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>186</td>\n",
       "      <td>166</td>\n",
       "      <td>397</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>27.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1420</td>\n",
       "      <td>790</td>\n",
       "      <td>1050</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>401</td>\n",
       "      <td>25</td>\n",
       "      <td>58</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>13</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>350</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>182</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>8.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>186</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>215</td>\n",
       "      <td>159</td>\n",
       "      <td>51</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>850</td>\n",
       "      <td>154</td>\n",
       "      <td>248</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>395</td>\n",
       "      <td>25</td>\n",
       "      <td>75</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>16.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>200</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "70    19  Female              0.7               0.2                   186   \n",
       "199    7  Female             27.2              11.8                  1420   \n",
       "213    8  Female              0.9               0.2                   401   \n",
       "334   13  Female              0.7               0.2                   350   \n",
       "335   13  Female              0.7               0.1                   182   \n",
       "455   21  Female              0.6               0.1                   186   \n",
       "460   22  Female              2.2               1.0                   215   \n",
       "485   22  Female              6.7               3.2                   850   \n",
       "537   10  Female              0.8               0.1                   395   \n",
       "568   20  Female             16.7               8.4                   200   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "70                        166                         397             5.5   \n",
       "199                       790                        1050             6.1   \n",
       "213                        25                          58             7.5   \n",
       "334                        17                          24             7.4   \n",
       "335                        24                          19             8.9   \n",
       "455                        25                          22             6.8   \n",
       "460                       159                          51             5.5   \n",
       "485                       154                         248             6.2   \n",
       "537                        25                          75             7.6   \n",
       "568                        91                         101             6.9   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "70       3.0                        1.20        1  \n",
       "199      2.0                        0.40        1  \n",
       "213      3.4                        0.80        1  \n",
       "334      4.0                        1.10        1  \n",
       "335      4.9                        1.20        1  \n",
       "455      3.4                        1.00        1  \n",
       "460      2.5                        0.80        1  \n",
       "485      2.8                        0.80        1  \n",
       "537      3.6                        0.90        1  \n",
       "568      3.5                        1.02        1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TODO\n",
    "#Dataframe all female who’s age is under 25 and dataset = 1\n",
    "df = data[(data[\"Gender\"] == \"Female\") & (data[\"Age\"] < 25) & (data[\"Dataset\"] == 1)]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Data Visualization\n",
    "\n",
    "Sometimes querying isn't enough, and you need to see the data laid out to understand more. Seaborn is a library which is a wrapper over matplotlib and is extremely convenient to use. For example, the below plot shows a box plot of alkaline_phosphotase of all patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.boxplot(x=data['Alkaline_Phosphotase']) #Box plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4. Using seaborn, plot a scatter plot between Age and Total_Protiens (note that this is mispelled in the dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TODO\n",
    "sns.scatterplot(x=data[\"Age\"],y=data[\"Total_Protiens\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5. Plot a grouped bar chart comparing the Alamine_Aminotransferase levels of patients with liver disease and patients without liver disease, categorized by gender. (Hint: Use the hue property of barplot for gender, and check this out: https://seaborn.pydata.org/generated/seaborn.barplot.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEICAYAAACwDehOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHUVJREFUeJzt3X14VPWZ//F3CGECGtBSKiJQiLQ3lBUfcEusrWLrQ0HRLb+VrfyERd1aqVYFra6CLUq9qq3Wp65KQYzQhf58qFthYWttXWpdI2wuWdFNbwGLEEFBEIEGBgLz++NMhoRC5iSZk5NMPq/r4po5J2e+555kyJ3vc0EqlUJERASgU9wBiIhI26GkICIiGUoKIiKSoaQgIiIZSgoiIpKhpCAiIhlKCiIikqGkICIiGUoKIiKS0TnuAJpq5cqVqUQiEXcYIiLtSk1NzUfDhw/vle26yJKCmY0A7nX3kfXOjQe+6+5npI+/BXwbqAV+6O6Ls5WbSCQYMmRINEGLiOSpysrK98JcF0nzkZndAswBiuudOwW4CihIH/cGrgfOBC4AfmRmqgKIiMQoqj6FtcDYugMz6wncA9xY75ovAq+6e9LdPwHWAMMiikdEREKIpPnI3Z8zswEAZlYIPAFMAXbXu6w78Em9451Aj2xlJ5NJqqqqchesiIhktEZH83Dgc8BjBM1JXzCzB4HfAyX1risBtmcrTH0KIlJn3759VFdXs2fPnrhDaTOKi4vp27cvRUVFDc5XVlaGen3kScHdlwNDAdK1h1+6+43pPoW7zawYSABDgLeijkdE8kd1dTUlJSUMGDCAgoKCuMOJXSqVYuvWrVRXVzNw4MBmlRHbPAV3/wB4GHiFoNYwzd2V7kUktD179tCzZ08lhLSCggJ69uzZoppTZDUFd18HlDV2zt1nA7OjikFE8p8SQkMt/X5oRrOIiGQoKUisKioqmDp1KhUVFXGHIsKGDRu4/vrrGTduHBMnTuTqq69m9erVzS5v7dq1TJgwIYcRRq/dLXMh+aW8vJzVq1dTU1NDWVlZ9heIRGT37t1MnjyZmTNncuqppwLw5ptvctdddzF//vyYo2s9SgoSq5qamgaPInF5+eWXKSsryyQEgGHDhjFv3jw2bdrEHXfcQTKZJJFIMHPmTPbv389NN91E79692bBhAyeddBJ33nknmzdv5uabbyaVStGr18GlhpYvX84DDzxAYWEh/fr146677mLRokU899xzHDhwgOuvv54zzjgjjrfegJKCiAjB8Nb+/ftnjidPnsyuXbvYvHkzvXv35sorr+Tss8/mtdde47777mPKlCmsW7eOJ554gq5du3LuueeyZcsWnnzySS666CLGjRvHkiVLWLhwIalUijvuuIMFCxbQs2dPHnzwQZ5//nk6d+5M9+7deeyxx2J85w0pKYiIAL179+attw5Olar7RT1u3DhWrlzJrFmzmDNnDqlUKjMxrH///hx99NEA9OrVi2QyyerVq7nkkksAOO2001i4cCHbtm1j8+bN3HhjsNLPnj17OPPMM+nfv3+z5xNERUlBRAT42te+xuzZs1m5ciWnnHIKAO+99x4ffPABw4YNY8qUKZx22mmsXbuWFStWAIcf/llaWsobb7zB4MGDWbVqFQDHHnssvXv35tFHH6WkpITf/e53dOvWjU2bNtGpU9sa76OkICICHHXUUTz22GPcf//93HfffdTW1tK5c2dmzpxJaWkpM2bMIJlMsmfPHqZNm3bEcm644QamTJnCkiVL6Nu3LwCdOnVi2rRpXH311aRSKY466ih+/OMfs2nTptZ6e6EVpFKpuGNokqqqqpTWPsofEydO5P333+eEE05g3rx5cYcj7UxVVZXWQjuMw31fKisrK4cPH356tte2rXqLiIjESklBREQylBRERCRDSUFERDKUFEREJENJQUTyRnLf/jZdXnugeQoikjcSRYUM/17uhjZX/mRi1muqq6u5+OKLGTp0aObciBEjuO6663IWx4QJE5gxYwYnnnhizso8EiUFEZEWGjRoUN6spKqkICISgfvvv58VK1aQSqWYNGkSo0aNYsKECZgZq1evplu3bpx++un88Y9/ZMeOHcydO5fCwkKmTZvGzp07+fjjj7n00ksZP358psydO3cybdo0Pv74YwCmT5+OmeU0biUFEZEWWrNmTYPNdC699FKqq6v55S9/STKZZNy4cZx55plAsBz39OnTueqqqyguLubJJ5/k1ltvZcWKFRx//PFceOGFnH/++Xz44YdMmDChQVJ4/PHHKSsrY/z48axbt47bbruNhQsX5vS9KCmIiLTQoc1Hs2fP5u23384kitraWjZu3AiQ6Xvo3r07gwYNyjxPJpN8+tOf5qmnnuLFF1/k6KOPpra2tsF93nnnHSoqKli6dCkAO3bsyPl7UVIQEcmx0tJSRowYwcyZMzlw4ACPPvpoZnG8xsydO5dTTjmF8ePHU1FRwbJly/6q3IsvvpgxY8awdetWnnnmmZzHrqQgInkjuW9/qBFDTSkvUVTY5Nd99atfZfny5YwfP56amhrOPffczL4LjTnnnHOYMWMGixYt4phjjqGwsJC9e/dmvn7NNdcwbdo0nn76aXbt2pXTEU51Ilsl1cxGAPe6+0gzOwV4BNgPJIGJ7v6hmX0L+DZQC/zQ3RdnK1erpOYXrZIqLaFVUg+vza2Sama3AHOA4vSph4DvuvtI4FfArWbWG7geOBO4APiRmSWiiEdERMKJakbzWmBsveNvuvvK9PPOwB7gi8Cr7p5090+ANcCwiOIREZEQIulTcPfnzGxAveNNAGb2JeA64CyC2sEn9V62E+iRrexkMklVVVVO45X41LWX7t27Vz9XabJ9+/axe/fuuMNoc/bt29fs/0+t1tFsZv8ATAMudPctZrYDKKl3SQmwPVs5iURCbYh5pEuXLplH/VylqaqqqujatWvcYbQ5RUVFh+tTCPXaVkkKZnY5QYfySHfflj69HLjbzIqBBDAEeKs14hERkcOLPCmYWSHwMLAe+FV6SvYyd/+BmT0MvELQtzHN3fdEHY+I5K9UbZKCzrkbr5Lr8tqDyJKCu68DytKHnzrCNbOB2VHFICIdS0HnBOvvOiln5fX//qqs17z++utMnDiRBx54gNGjR2fOjxkzhqFDh3LPPff81Wt+9atf8e6773LzzTfnLNZc0X4KIiItVFpayuLFB6dZuXu77QDXjGYRkRYaPHgw69atY8eOHXTv3p0XXniBMWPGsGnTJn7xi1/w4osvUltbS0lJCY888kiD186fP5/FixdTUFDA6NGjmTgxdzOym0M1BRGRHDjvvPP47W9/SyqV4s033+TUU0/lwIEDbN++nfLychYsWEBtbS2rVh1sklqzZg1LlixhwYIFLFiwgJdeeol33303xnehmoKISE6MGTOGGTNm0K9fP04/PVhNolOnThQVFTF16lS6devGBx980GDl03feeYeNGzcyadIkAD755BPWr19PaWlpHG8BUFIQEcmJfv36UVNTw/z585k6dSobNmxg165dvPTSSzzzzDPs3r2bsWPHUn+9udLSUgYNGsScOXMoKCigvLycz3/+8zG+CyUFEckjqdpkqBFDTSmvKUNSR48eza9//WsGDhzIhg0bKCwspGvXrowdO5YuXbrQq1cvNm/enLl+8ODBnHHGGVx22WXs3buXYcOGcdxxx+Us/uaIbJXUqGiV1PyiVVKlJbRK6uG1uVVSRUSkfVJSEBGRDCUFEWnX2lsTeNRa+v1QUhCRdqu4uJitW7cqMaSlUim2bt1KcXFx9ouPINToo/SidpOA/sDLwFvu/lGz7yoikgN9+/alurqaLVu2xB1Km1FcXEzfvn2b/fqwQ1JnARuB84D/BuYBoxt9hYhIxIqKihg4cGDcYeSVsM1HJ7r794Hd7r6IEDukSfuQqk3GHUKboe+FSPiaQmcz+zSAmZUAB6ILSVpTrpcabqrabZ8COlO77b1Y44BwyySL5LuwSWE68CpwPFAB3BhZRCIiEptQScHdlwFmZr2Aj929NttrRESk/QnVp2Bml5rZNwk6l983s7a3XZCIiLRY2I7mm4HfApcTDEsdE1lEIiISm7BJYU/6cae7J4GSiOIREZEYhU0KfyaYnzDXzH4AvB5dSCIiEpdQScHdJwEnuftiYJa7T440KhERiUXYZS7KgCvMrAgoMLM+7n5BtKGJiEhrCztP4WHgAeDvgVVAl2wvMLMRwL3uPtLMBgHlQAp4C7jW3Q+km6IuBGqBG919edPfgoiI5ErYPoXt7r4Q2OHuM4BGV1sys1uAOUDdUn0/Baa7+1eAAuASMzsNOBsYAXwT+Jemhy8iIrkUNimkzGwo0M3MDOid5fq1wNh6x8OBZennS4FzgS8DL7p7yt3XEyyl0St86CIikmthm4+mAkMJmpEWAI81drG7P2dmA+qdKnD3ugXPdxIsqNcd2Frvmrrzja6Bm0wmqaqqChm2ZKP9bRvSZ0s6urBJ4Sp3n5p+PrwZ96m/gF4JsB3YQcP5DnXnG5VIJPSLTCKjz5bkq8rKylDXhW0+GmJmxzQ/HN4ws5Hp56OAVwgW2LvAzDqZWX+gkzbuERGJV9iawheArWa2hWAEUcrd+zThPjcBs82sC1AFPOvu+83sFeA1guR0bRPKExGRCIRdJfWzTS3Y3dcBZenn7xCMNDr0mhnAjKaWLSIi0Qg7eW0o8DhwDPCvBHs0L44yMBERaX1h+xQeBq4APgKeQH/di4jkpbBJAXdfQ9CXsIVg+KiIiOSZsElhm5l9GzgqvdlO1qGjIiLS/jSaFMysR/rpVcBAguaj09PHIiKSZ7J1NC8CziJY2E7LZYuI5LlsSWG3ma0APmdmJ9f/grt/Kbqw8ltFRQVPP/0048aNo6ysLO5wREQysiWFUUAfYBbwnejD6RjKy8tZvXo1NTU1Sgoi0qY02qfg7gfcvRr4B2A/wV7NE1sjsHxWU1PT4FFEpK0IO/roFwQL4f0E2Af8PLKIREQkNmGTwrHAC0Bfd78HSEQXkoiIxCVsUuhCsKhdpZl9ATg6upBERCQuYZPCzcBngLuBc1Cns4hIXgqVFNz9VeA+gt3SFgEfRBmUiIjEI+wqqY8SDE/dBBQQ7KmgeQoiInkm7CY7XwROdPcDWa8UEZF2K2yfwhqgOMpAREQkfmFrCv2B98xsTfo4pWUuRETyT9ikcFmkUUiHVVyYavAoIvEKmxSKgEvTjwUE6yF9O6qgpOP4xoC/8B8buvH1flryQ6QtCJsU5hEMRf0ysBFNXpMcObnnXk7uuTfuMEQkLWxHc427/wiodvdJwHHRhSQiInEJW1MoMLPewNFmdhTwqabeyMyKgKeAAQQrrn4LqAXKCeY9vAVcq2GvIiLxCVtTuBP4O4LVUv8MLG3GvUYDndOjlu4iWDLjp8B0d/8KQV/FJc0oV0REciT05DV3vy/9/DPNvNc7QGcz60SwXMY+oAxYlv76UuB84Plmli8iIi0UtqYw2swKW3ivXQRNR38CZgMPAwXuXjcWcSfQo4X3EBGRFghbU+gFbDSzPxO0/zdn8toU4DfufpuZ9QN+T7Akd50SYHu2QpLJJFVVVU28dduyd+/ezGPc72XIkCGx3r+tifvnIRK3sElhLFB/3GCTO5qBjwmajAC2Ecx5eMPMRrr7fxIsuPdytkISiUS7/0XWpUuXzGN7fy/5Rj8PyVeVlZWhrms0KaRHHHUnmKcwgaAzuBMwi2CRvKZ4AJhrZq8Q1BBuB/4bmG1mXYAq4NkmlikiIjmUraZQBtwAGAf3ZT4A/KapN3L3XcC4w3zp7KaWJSIi0Wg0Kbj7vwH/Zmaj3X1JK8UkIiIxCdunUG1mK4C+BLuuXenub0QXloiIxCHskNSHgH9y9+OBK4CfRReSiIjEJWxS6OTu/wPg7isJlqcQEZE8E7b5aJ+ZXQS8ApwFJKMLSURE4hK2pnAV8I/AqwRDU78VWUQiIhKbUDUFd3+PYJMdERHJY6GSgpndDtwC1BBMYEu5e58oAxMRkdYXtk9hHNDH3bVnoohIHgvbp7AO2B1hHCIi0gaErSl0AVaZ2ar0ccrdx0cUk4iIxCRsUrg30ihERKRNaLT5KD03AYIF8Q79JyIieSZbTaFn+vH4Q86nDr1QRETav2yrpD6Vfno38AWgOPKIREQkNmH7FP6doLO5brvMFMFubO1Sct9+EkUt3XJaRCT/hE0Kxe6eN5vhJIoKGf69ebHdv+SjnRQC6z/aGWscAJU/mRjr/UWkbQmbFP5gZhcQbJkJgLuvjyYkERGJS9ikcBzwIA2bj74USUQiIhKbsEnB3H1IpJGIiEjswiaFVWZWBrxBejiqu++NLCoREYlF2KRwFnBhveMUUJr7cEREJE5h91MYVvfczIqAv48sIhERiU3YmgJmdjxwDXAl8D/AwqbezMxuAy4mmPPwKLAMKCeoebwFXOvuB5paroiI5EbWpbPN7Gwze4Zgf+bLgC+5+0VZXna4ckYSjFg6Ezgb6Af8FJju7l8h2LznkqaWKyIiuZNtQbxKgv2YZwGfB9a6+4Zm3usCYBXwPLAIWAwMJ6gtACwFzm1m2SIikgPZmo+WE/x1Pwp4n5YthPdp4LPARcBA4AWgk7vXlbkT6JGtkGQySVVVVbbLGjVkiEbXyuG19LMl0t5lWxBvspl1JdiO8+fA35jZZOD/ufu2Jt5rK/Cn9FBWN7M9BE1IdUo4ODnuiBKJhH6pS2T02ZJ8VVlZGeq6rH0K7r7b3Z9Kt/ufAZxI0NHcVH8Evm5mBWbWBzgK+F26rwGC2sgrzShXRERyJOwezQC4+5/c/WbScxTM7PkmvHYxweS35QR9CtcCNwF3mtlrBCOSnm1KPCIikluhh6TW5+770k+PaeLrbjnM6bxZfVVEpL1rUk3hMLQDm4hIHmlpUhARkTyipCAiIhktTQof5yQKERFpE0J1NJvZCcC9QC+CEUJvuvvr7v5/ogxORERaV9iaws+BuQTDRv8APBRZRCIiEpuwSaHY3X8PpNzdgT0RxiQiIjEJmxSSZnYBUJjegU1JQUQkD4VNClcDVxAsanczMDmyiEREJDZhd16rBr4ZcSwiIhKzsKOPbgduAWoINsNJuXufKAMTEZHWF3bto3FAH3eviTIYERGJV9g+hXXA7gjjEBGRNiBsTaELsMrMVqWPU+4+PqKYREQkJmGTwr2RRiEiIm1Co81HZnZR3dPD/BMRkTyTrabQM/14/CHntY9CC6Q6dW7wKCLSVjT6W8ndn0o/vRv4AlAceUQdwJ4+p5L48G2Sxw2NOxQRkQbC/qn67wSdzdvTxylgbCQRdQC1PfpS26Nv3GGIiPyVsEmh2N21l7KISJ4LmxT+kF4Qr6ruhLuvjyYkERGJS9ikcBzwIA2bj74USUQiIhKbsEnB3H1IpJGIiEjswiaFVel9FN4gPRzV3fc254Zm9hmgEjgPqAXK02W+BVzr7geaU66IiLRc2LWPzgIWAn8CPP3YZGZWBMzi4DpKPwWmu/tXCFZfvaQ55YqIRKGiooKpU6dSUVERdyitJux+CsNydL/7gMeB29LHw4Fl6edLgfOB53N0LxGRFikvL2f16tXU1NRQVlYWdzitIux+ChcD1wJFBH/R92xqojCzScAWd/+NmdUlhQJ3r5sdvRPoka2cZDJJVVVVtssaNWSIukfk8Fr62ZL8sn379sxjR/lshO1T+D7wXeAa4GWC/oCmuhJImdm5wCnAPOAz9b5ewsHRTUeUSCT0S10io8+W1NelS5fMY3v/bFRWVoa6LmyfwlZ3fw3A3cuBJk/Hdfez3P1sdx8JrAQmAkvNbGT6klHAK00tV0REcidsUkia2VlAUXoS26EL5DXXTcCdZvYawTIaz+aoXBFppo7YuSoHhW0+mgwMBn4IzCRoTmq2dG2hjpbPEGlDOmLnqhzUaFIws8/XO9yQfrwdLZ0tkrdqamoaPErHkq2mMKve87pE0Av4HFpGW0Qk72TbT+Gcuudm9kXgOoJ9FZ6IOC4R6eBStUkKOifiDqNNaM3vRbbmoy7AZcB3gL1Ad2Cgu+9u7HUiIi1V0DnB+rtOijWG2m2fAjpTu+29WGPp//1VrXavbKOP1gHDgMvTS1FsVEIQEclf2foUHgLGAwPMbA7BbGYREclTjdYU3P1edz8ZeJggOfytmd1rZn/TKtGJiEirCjV5zd2XufsE4ESgGpgfaVQiIhKLsJPXAHD37cAj6X8iInmtuDDV4LEjCLvMhYhIh/ONAX9hcI+9fGPAX+IOpdU0qaYgItFK7ttPoqgw7jAk7eSeezm5Z7M2mWy3lBRE2pBEUSHDvzcv1hhKPtpJIbD+o52xxlL5k4mx3bsjU/ORiIhkKCmIiEiGkoKIiGQoKYiISIaSgoiIZCgpiIhIhpKCiIhkKCmISAOpTp0bPErHoqQgIg3s6XMq+47uzZ4+p8YdisRAfwqISAO1PfpS26Nv3GFITFotKZhZETAXGAAkgB8C/wuUAyngLeBadz/QWjGJiEhDrdl8dDmwNb2t5yjgZ8BPgenpcwXAJa0Yj4iIHKI1k8IzwB31jmuB4cCy9PFS4NxWjEdERA7Ras1H7r4LwMxKgGeB6cB97l63e8VOoEe2cpLJJFVVVS2KZciQIS16veSvln62WkqfTTmS1vpstmpHs5n1A54HHnX3BWb243pfLgG2ZysjkUjoP45ERp8taata+tmsrKwMdV2rNR+Z2XHAi8Ct7j43ffoNMxuZfj4KeKW14hERkb/WmjWF24FjgTvMrK5v4QbgYTPrAlQRNCuJiEhMWrNP4QaCJHCos1srBhERaZxmNIuISIaSgoiIZCgpiIhIhpKCiIhkKCmIiEiGkoKIiGQoKYiISIaSgoiIZCgpiIhIhpKCiIhkKCmIiEiGkoKIiGQoKYiISIaSgoiIZCgpiIhIhpKCiIhkKCmIiEiGkoKIiGQoKYiISIaSgoiIZCgpiIhIhpKCiIhkKCmIiEhG57gDMLNOwKPAyUAS+Cd3XxNvVCIiHVNbqCn8HVDs7mcA/wzcH3M8IiIdVltICl8G/gPA3SuA0+MNR0Sk4ypIpVKxBmBmc4Dn3H1p+ng9UOrutYe7vrKycgvwXiuGKCKSDz47fPjwXtkuir1PAdgBlNQ77nSkhAAQ5k2JiEjztIXmo1eB0QBmVgasijccEZGOqy3UFJ4HzjOz/wIKgCtijkdEpMOKvU9BRETajrbQfCQiIm2EkoKIiGS0hT4FiYmZjQDudfeRccciUsfMioC5wAAgAfzQ3V+INagORDWFDsrMbgHmAMVxxyJyiMuBre7+FWAU8LOY4+lQlBQ6rrXA2LiDEDmMZ4A76h0fcd6S5J6SQgfl7s8B++KOQ+RQ7r7L3XeaWQnwLDA97pg6EiUFEWlzzKwf8DIw390XxB1PR6KOZhFpU8zsOOBF4Dp3/13c8XQ0Sgoi0tbcDhwL3GFmdX0Lo9x9d4wxdRia0SwiIhnqUxARkQwlBRERyVBSEBGRDCUFERHJUFIQEZEMDUkVSTOzkcDTwP8SbPhUBDzo7k8f4fr+wMnuvigH9/4U8HVN1JK4qaYg0tDv3X2ku58NnA/camanHOHarwJn5ui+w4CLc1SWSLNpnoJIWrqmcI27f7PeuauBgUAvoB/QE1gKzADeBroB1wGfAD9Iv6wbMBFYT1Dz6AF0BW5x9/80s0uBqcB+4I/u/s9m9lvgZGC6u/882ncqcmSqKYg07kPgVKDC3S8AvgxMdvf9wD3AgvRa/0OBy939q8ALwKXAiUBvYAwwHuiWbia6E/iau38ZOMHMzgPuJqilKCFIrNSnINK4zwL/BfytmZ0D7CDY+OVQ7wMPm9ku4ATgVXd/28z+BVhI0D/xMDCIoNaxxMwASoBSwKN+IyJhqKYgcgTppZu/RdA0tN3d/y9wP8Ff/AXAAQ7+H5oDXOHuk4CNQIGZnQSUuPuFwD8CjwB/BjYA56V3vHsEeP2QskRioz4FkbRDRh/tJ6hJP0TwV/wvgZ3AXwj6Fr4GfCZ9/gfAF4GvAx8TNDltBb4L/ALoD+wFZrn7fDO7HPgOUAisA64gWADupfQ1D0b+ZkWOQElBREQyVF0VEZEMJQUREclQUhARkQwlBRERyVBSEBGRDCUFERHJUFIQEZEMJQUREcn4/xINj5+KdWCfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TODO\n",
    "#compare Alamine_Aminotransferase for 1.with liver disease/2.without liver disease\n",
    "sns.barplot(x=\"Dataset\",y=\"Alamine_Aminotransferase\",hue = \"Gender\",data = data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the correlation heatmap for the different features for some more inspiration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = data.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q6. You can try out any other plots here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection and Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the knowledge that we've gathered above, let's decide on the best features that we should include for creating a model. Using the knowledge that you have about dimensionality and feature selection, pick an appropriate number of features for the dataset. There is no right or wrong here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Feature Selection\n",
    "\n",
    "Q7. Make a reduced dataset new_data by selecting only relevant columns from the original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_Protiens  Albumin  Albumin_and_Globulin_Ratio\n",
       "0             6.8      3.3                        0.90\n",
       "1             7.5      3.2                        0.74\n",
       "2             7.0      3.3                        0.89\n",
       "3             6.8      3.4                        1.00\n",
       "4             7.3      2.4                        0.40"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TODO \n",
    "\n",
    "new_data = data[[\"Total_Protiens\",\"Albumin\",\"Albumin_and_Globulin_Ratio\"]]\n",
    "\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Create Training and Validation Data Split\n",
    "\n",
    "Q8. Create training and validation split on data. Check out train_test_split() function from sklearn to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146\n",
      "437\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#TODO\n",
    "train = new_data\n",
    "label = data[[\"Dataset\"]]\n",
    "\n",
    "X_train,X_val,y_train,y_val = train_test_split(train,label)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Feature Scaling\n",
    "\n",
    "We always scale the features after splitting the dataset because we want to ensure that the validation data is isolated. This is because the validation data acts as new, unseen data. Any transformation on it will reduce its validity.\n",
    "\n",
    "Q9. Although there are many methods to scale data, let's use MinMaxScaler from sklearn. Scale the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.43478261 0.43478261 0.28      ]\n",
      " [0.68115942 0.69565217 0.36      ]\n",
      " [0.50724638 0.47826087 0.28      ]\n",
      " ...\n",
      " [0.49275362 0.47826087 0.28      ]\n",
      " [0.55072464 0.5        0.24      ]\n",
      " [0.63768116 0.26086957 0.04      ]]\n",
      "mean is [0.54193613 0.48358372 0.26040553]\n",
      "index is (array([11, 55, 74]), array([2, 2, 2]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#TODO\n",
    "scaler = MinMaxScaler()           #Instantiate the scaler\n",
    "scaled_X_train = scaler.fit_transform(X_train)     #Fit and transform the data\n",
    "scaled_X_train\n",
    "print(scaled_X_train)\n",
    "col_mean = np.nanmean(scaled_X_train, axis = 0)\n",
    "print(\"mean is\",col_mean)\n",
    "inds = np.where(np.isnan(scaled_X_train)) \n",
    "print(\"index is\",inds)#返回值是 (x_corr,y_corr)\n",
    "scaled_X_train[inds] = np.take(col_mean, inds[1])\n",
    "\n",
    "#scaled_X_train = np.nan_to_num(scaled_X_train)\n",
    "np.isnan(scaled_X_train).any()#True:contains nan   False:no nan\n",
    "\n",
    "#scaled_Y_train = scaler.fit_transform(y_train)     #Fit and transform the data\n",
    "#scaled_Y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 6: Model Creation\n",
    "\n",
    "Now we are finally ready to create a model and train it. Remember that this is a two-class classification problem. We need to select a classifier, not a regressor. Let's analyze two simple models, DecisionTreeClassifier and Gaussian Naive Bayes Classifier. We will cover both of these models in more depth later on in the course, for now, have a read of the following articles that give explanations of how to implement both models in scikitlearn:\n",
    "\n",
    "Decision Trees: https://www.datacamp.com/community/tutorials/decision-tree-classification-python\n",
    "\n",
    "Naive Bayes: https://www.datacamp.com/community/tutorials/naive-bayes-scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q10. Instantiate and train a DecisionTreeClassifier on the given data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "#TODO\n",
    "clf_dt = DecisionTreeClassifier()        #Instantiate a DecisionTreeClassifier model and fit it to the training data using fit function\n",
    "clf = clf_dt.fit(scaled_X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q11. Instantiate and train a GaussianNB on the given data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "#TODO\n",
    "clf_nb = GaussianNB()            #Instantiate a DecisionTreeClassifier model and fit it to the training data using fit function\n",
    "y_train = np.array(y_train)\n",
    "y_train.reshape(437,)\n",
    "# print(type(y_train))\n",
    "clf_nb.fit(scaled_X_train,y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "These models are now capable of 'predicting' whether a patient has liver disease or not. But we need to evaluate their performance. Since it is a two-class classification problem, we can use accuracy. However, let us also use some additional metrics for better analysis, precision,recall, and f1score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 7: Performance Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q12. Using the accuracy_score function, determine the accuracy of the two classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean is [0.6867995  0.5491146  0.38252244]\n",
      "index is (array([85]), array([2]))\n",
      "The accuracy of Decision Tree: 0.5958904109589042 %\n",
      "The accuracy of Gaussian Naive Bayes: 0.6506849315068494 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "#TODO\n",
    "scaled_X_val = scaler.fit_transform(X_val)     #Fit and transform the data\n",
    "col_mean = np.nanmean(scaled_X_val, axis = 0)\n",
    "print(\"mean is\",col_mean)\n",
    "inds = np.where(np.isnan(scaled_X_val)) \n",
    "print(\"index is\",inds)#返回值是 (x_corr,y_corr)\n",
    "scaled_X_val[inds] = np.take(col_mean, inds[1])\n",
    "np.isnan(scaled_X_val).any()\n",
    "# scaled_X_val = None                  #Fit and transform the validation set using the MinMaxScaler\n",
    "y_pred_dt = clf_dt.predict(scaled_X_val)\n",
    "y_pred_nb = clf_nb.predict(scaled_X_val)\n",
    "\n",
    "# #Use accuracy score and determine accuracy of both classifiers\n",
    "acc_dt = accuracy_score(y_val,y_pred_dt)                            \n",
    "acc_nb = accuracy_score(y_val,y_pred_nb)  \n",
    "\n",
    "print(\"The accuracy of Decision Tree: {} %\".format(acc_dt))\n",
    "print(\"The accuracy of Gaussian Naive Bayes: {} %\".format(acc_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q13. Determine the precision and recall using precision_score and recall_score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The precision of Decision Tree: 0.6551724137931034 %\n",
      "The precision of Gaussian Naive Bayes: 0.6506849315068494 %\n",
      "The recall of Decision Tree: 0.8 %\n",
      "The recall of Gaussian Naive Bayes: 1.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score,recall_score\n",
    "\n",
    "#    +   -\n",
    "#T   tp  fp\n",
    "#F   fn  tn\n",
    "\n",
    "#pres:tp/(tp+fp)\n",
    "#recall:tp/(tp+fn)\n",
    "prec_dt = precision_score(y_val,y_pred_dt)\n",
    "prec_nb = precision_score(y_val,y_pred_nb)\n",
    "\n",
    "recall_dt = recall_score(y_val,y_pred_dt,average='binary')\n",
    "recall_nb = recall_score(y_val,y_pred_nb,average='binary')\n",
    "#macro：计算二分类metrics的均值，为每个类给出相同权重的分值。当小类很重要时会出问题，因为该macro-averging方法是对性能的平均。另一方面，该方法假设所有分类都是一样重要的，因此macro-averaging方法会对小类的性能影响很大。\n",
    "#weighted:对于不均衡数量的类来说，计算二分类metrics的平均，通过在每个类的score上进行加权实现。\n",
    "#micro：给出了每个样本类以及它对整个metrics的贡献的pair（sample-weight），而非对整个类的metrics求和，它会每个类的metrics上的权重及因子进行求和，来计算整个份额。Micro-averaging方法在多标签（multilabel）问题中设置，包含多分类，此时，大类将被忽略。\n",
    "\n",
    "\n",
    "print(\"The precision of Decision Tree: {} %\".format(prec_dt))\n",
    "print(\"The precision of Gaussian Naive Bayes: {} %\".format(prec_nb))\n",
    "print(\"The recall of Decision Tree: {} %\".format(recall_dt))\n",
    "print(\"The recall of Gaussian Naive Bayes: {} %\".format(recall_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q14. Determine the F1-score of the two classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The F1-score of Decision Tree: 0.7203791469194314 %\n",
      "The F1-score of Gaussian Naive Bayes: 0.7883817427385892 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "#F1 = 2 * (precision * recall) / (precision + recall)\n",
    "f1_dt = f1_score(y_val,y_pred_dt)\n",
    "f1_nb = f1_score(y_val,y_pred_nb)\n",
    "\n",
    "print(\"The F1-score of Decision Tree: {} %\".format(f1_dt))\n",
    "print(\"The F1-score of Gaussian Naive Bayes: {} %\".format(f1_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have officially solved a machine learning problem. However, the question of making an effective model is still in question.Try to get the f1 score of any of the classifiers to 85%. Try to use some other scalers if need be. You can experiment with some other data cleaning techniques like outlier removal. You can try using other classifiers, but remember that in the end, no matter how good the classifier, if the data quality sucks, the performance will not be optimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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